Role of Fuzzy in Multimodal Biometrics System (original) (raw)
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Fuzzy Fusion in Multimodal Biometric Systems
Lecture Notes in Computer Science, 2007
Multimodal authentication systems represent an emerging trend for information security. These systems could replace conventional mono-modal biometric methods using two or more features for robust biometric authentication tasks. They employ unique combinations of measurable physical characteristics: fingerprint, facial features, iris of the eye, voice print, hand geometry, vein patterns, and so on. Since these traits are hardly imitable by other persons, the aim of these multibiometric systems is to achieve a high reliability to determine or verify person's identity. In this paper a multimodal biometric system using two different fingerprints is proposed. The matching module integrates fuzzy logic methods for matching score fusion. Experimental trials using both decision level fusion and matching score level fusion were performed. Experimental results show an improvement of 6.7% using the matching score level fusion rather then a mono-modal authentication system.
Multimodal fuzzy fusion for biometric identity management
2007
Biometric identity management based only on the single biometric modality is not accurate or robust enough to be used in uncontrolled environments. This paper describes a fusion of face and voice biometric traits, based on fuzzy logic approach for speaker identity verification. In this approach, a scheme based on membership function and fuzzy integral is proposed to fuse information from the two modalities. Equal Error rate is used to evaluate the fusion scheme. Experimental results show the fusion scheme improves identity verification performance substantially and makes the system robust to environmental degradations such as acoustic noise and visual compression artefacts.
MULTIMODAL BIOMETRIC SYSTEMS – STUDY TO IMPROVE ACCURACY AND PERFORMANCE
Biometrics is the science and technology of measuring and analyzing biological data of human body, extracting a feature set from the acquired data, and comparing this set against to the template set in the database. Experimental studies show that Unimodal biometric systems had many disadvantages regarding performance and accuracy. Multimodal biometric systems perform better than unimodal biometric systems and are popular even more complex also. We examine the accuracy and performance of multimodal biometric authentication systems using state of the art Commercial Off-The-Shelf (COTS) products. Here we discuss fingerprint and face biometric systems, decision and fusion techniques used in these systems. We also discuss their advantage over unimodal biometric systems.
A Novel Fusion Method for Multimodal Biometric System against Spoofing Attack
Recently with development of information resources, the need for security and data protection is doubled. One security solution is use of biometric-based authentication systems. Multi-modal biometric system is the combination of several different types of biometric traits that can be a good alternative to Unimodal biometric systems to reduce vulnerabilities and increase the security level of biometrical systems, however, recent researches shown that multimodal biometric systems can be cracked by that forging even single biometric trait can. In this paper, we investigate performance of a multimodal system composed of face and ngerprint, whose scores are fused using the well-known sum, product weighted sum, likelihood ratio (LLR) and fuzzy logic rules under different spoofing attack scenarios and then we propose a robustness novel game theory based fusion method that can increase the security of multimodal biometric systems. Our result shown that even in the worst scenario, proposed fusion method is more robust against spoofing attack when compared with other existing methods and also the false acceptance rate (FAR) can remarkably decrease.
Review On: Multimodal Biometric Fusion
Biometric Fusion combined multiple data from multiple sources so that accuracy, efficiency and robustness of a biometric system can be improved. Multimodal biometric systems perform better than uni-modal biometric systems as it removes the limitations of single biometric system. In this review paper, different feature extraction algorithms (PCA, ICA) are discussed along with GA (Genetic Algorithm).
Multimodal Biometric System Fusion using Fingerprint , Face and Hand Geometry with Fuzzy Logic Mr
2015
Multimodal biometric identification system is more power full, more accurate, less noisy data than the single biometric model. In unimodal such as face, finger, iris, retina all are decade according to time pass or some changes may be applied therefore multimodal is given better performance. In this paper use fingerprint, hand geometry and face used as multibiometric and find good result with high accuracy using fuzzy logic.
Comparative Analysis of Multimodal Biometrics
2017
Unimodal biometrics usage has certain limitations like no universality and sensitivity to attack. Multimodal biometrics assures better accuracy and development of a stringent system to protect data from unauthorized user access. In multi modal biometric systems, the biometric traits are fused at different levels like feature level, match score level, decision level with the help of various fusion methodologies; some of which can be listed as concatenation, weighted summation, product, min, max, majority voting etc. This paper summarizes work published by different researchers on multi biometric systems with respect to modalities, fusion ways, fusion levels, database, dataset size, algorithms, performance, etc. The paper concludes with future direction in the security measures using multimodal biometric systems. Critical analysis of various kinds of work is done with respect to factors like modalities, fusion approaches, database, performance and suggestions are given for future rese...
Multimodal Biometric Systems: A Comparative Study
Arabian Journal for Science and Engineering, 2016
Biometrics technology stands as one of the major backbones that had united biosciences and technology representing an instrument for security and forensics researchers to develop more accurate, robust and confident systems. Starting from uni-modal biometrics as finger print, face, speech and iris passing through multimodal biometrics based on uni-biometrics fused by different fusion techniques as feature level, score level and decision level fusion techniques, biometrics were still one of the most investigated technologies. From here in this paper, we tried to build the base for researchers whom are interested in biometric systems through introducing a comparative study of most used and known uni-and multimodal biometrics such as face, iris, finger vein, face and iris multimodal, face, finger print and finger vein multimodal. Through this comparative study, a comparative model is based on principal component analysis feature extractor and Euclidean distance matcher applied using MATLAB. This model was trained and tested in two different modes homogenous data using SDUMLA-HMT database and heterogeneous mode extracting 106 frontal single face image from CASIA-FACEV5 while the reminder biometrics under consideration from SDUMLA-HMT. Feature level and score level fusions were tested in both modes on all multimodal systems under consideration.
Multimodal Biometric Authentication System: Challenges and Solutions
Global journal of computer science and technology, 2011
Biometric technologies are automated methods for measuring and analyzing biological data, extracting a feature set from acquired data and comparing this set against to the templates set in the database. Unimodal biometric system have variety of problems such as noisy data, spool attacks etc. Multimodal biometrics refers the combination of two or more biometric modalities in a single identification. Most biometric verification systems are done based on knowledge base and token based identification these are prone to fraud. Biometric authentication employs unique combinations of measurable physical characteristics-fingerprint, facial features , iris of the eye, voice print and so on-that cannot be readily imitated or forged by others. This paper discuss the various scenarios that are possible in multi model biometric system , the level of fusion that are plausible and the integration strategic that can be adopted to consolidate information. Fusion methods include processing biometric madalitics sequential until an acceptable match is obtained.
Biometric recognition systems have advanced significantly in the last decade and their use in specific applications will increase in the near future. The ability to conduct meaningful comparisons and assessments will be crucial to successful deployment and increasing biometric adoption. Even the best modality and unimodal biometric systems were unable to fully address the problem of accuracy and performance in terms of their false accept rate (FAR) and false reject rate (FRR). Although multimodal biometric systems were able to mitigate some of the limitations encountered in unimodal biometric systems, such as non-universality, distinctiveness, non-acceptability, noisy sensor data, spoof attacks, and performance, the issue of low accuracy and performance still persists. In this paper, we review research papers focused on the accuracy and performance enhancement in information fusion of face and fingerprint biometric recognition systems, determine the main features of the selected methods, and then point out their merits and shortcomings. Finally, we propose a novel approach in mitigating the problem of accuracy and performance of information fusion of multimodal biometric systems. This approach makes use of multilayer perceptron neural networks in training and testing of the network while also proposing the use of the most common used modalities (face and fingerprint) in biometric arena.